In this section, we study the model-PD and the model-PFI, and provide variance and confidence interval estimators.
With model-PD and model-PFI, we refer to the original proposals for PD [1]} and PFI [2]}, [3]} for fixed models.
Conditioning on a given model \(\hat{f}\)  ignores the model variance due to the learning process.
Only the variance due to Monte Carlo integration can considered in this case.
